"""This tutorial introduces the LeNet5 neural network architecture
using Theano.  LeNet5 is a convolutional neural network, good for
classifying images. This tutorial shows how to build the architecture,
and comes with all the hyper-parameters you need to reproduce the
paper's MNIST results.

This implementation simplifies the model in the following ways:

 - LeNetConvPool doesn't implement location-specific gain and bias parameters
 - LeNetConvPool doesn't implement pooling by average, it implements pooling
   by max.
 - Digit classification is implemented with a logistic regression rather than
   an RBF network
 - LeNet5 was not fully-connected convolutions at second layer

References:
 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:
   Gradient-Based Learning Applied to Document
   Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
   http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

"""

from __future__ import print_function

import os
import sys
import timeit
import six.moves.cPickle as pickle
import numpy
import gzip
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv2d

from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer


class LeNetConvPoolLayer(object):
    """Pool Layer of a convolutional network """

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.dtensor4
        :param input: symbolic image tensor, of shape image_shape

        :type filter_shape: tuple or list of length 4
        :param filter_shape: (number of filters, num input feature maps,
                              filter height, filter width)

        :type image_shape: tuple or list of length 4
        :param image_shape: (batch size, num input feature maps,
                             image height, image width)

        :type poolsize: tuple or list of length 2
        :param poolsize: the downsampling (pooling) factor (#rows, #cols)
        """

        assert image_shape[1] == filter_shape[1]
        self.input = input

        # there are "num input feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = numpy.prod(filter_shape[1:])
        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) //
                   numpy.prod(poolsize))
        # initialize weights with random weights
        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
        self.W = theano.shared(
            numpy.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )

        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        # convolve input feature maps with filters
        conv_out = conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            image_shape=image_shape
        )

        # downsample each feature map individually, using maxpooling
        pooled_out = downsample.max_pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True
        )

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        # store parameters of this layer
        self.params = [self.W, self.b]

        # keep track of model input
        self.input = input

def get_pickling_errors(obj,seen=None):
    if seen == None:
        seen = []
    try:
        state = obj.__getstate__()
    except AttributeError:
        return
    if state == None:
        return
    if isinstance(state,tuple):
        if not isinstance(state[0],dict):
            state=state[1]
        else:
            state=state[0].update(state[1])
    result = {}    
    for i in state:
        try:
            pickle.dumps(state[i],protocol=2)
        except pickle.PicklingError:
            if not state[i] in seen:
                seen.append(state[i])
                result[i]=get_pickling_errors(state[i],seen)
    return result        
class cnn(object):
    def __init__(self,rng, input, batch_size,nkerns):
    # Reshape matrix of rasterized images of shape (batch_size, 3 *32 * 32)
    # to a 4D tensor, compatible with our LeNetConvPoolLayer
    # (3*(32, 32)) is the size of MNIST images.
        self.layer0_input = input.reshape((batch_size, 3, 32, 32))

        # Construct the first convolutional pooling layer:
        # filtering reduces the image size to (96-5+1 , 32-5+1) = (28, 28)
        # maxpooling reduces this further to (96/2, 28/2) = (14, 14)
        # 4D output tensor is thus of shape (batch_size, nkerns[0], 14, 14)



        self.layer0 = LeNetConvPoolLayer(
            rng,
            input=self.layer0_input,
            image_shape=(batch_size, 3, 32, 32),
            filter_shape=(nkerns[0], 3, 5, 5),
            poolsize=(2, 2)
        )


        # Construct the second convolutional pooling layer
        # filtering reduces the image size to (14-5+1, 14-5+1) = (10, 10)
        # maxpooling reduces this further to (10/2, 10/2) = (5, 5)
        # 4D output tensor is thus of shape (batch_size, nkerns[1], 5, 5)
        self.layer1 = LeNetConvPoolLayer(
            rng,
            input=self.layer0.output,
            image_shape=(batch_size, nkerns[0], 14, 14),
            filter_shape=(nkerns[1], nkerns[0], 5, 5),
            poolsize=(2, 2)
        )

        # the HiddenLayer being fully-connected, it operates on 2D matrices of
        # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
        # This will generate a matrix of shape (batch_size, nkerns[1] * 5 * 5),
        # or (500, 50 * 5 * 5) = (500, 1250) with the default values.
        self.layer2_input = self.layer1.output.flatten(2)

        # construct a fully-connected sigmoidal layer
        self.layer2 = HiddenLayer(
            rng,
            input=self.layer2_input,
            n_in=nkerns[1] * 5 * 5,
            n_out=500,
            activation=T.tanh
        )

        # classify the values of the fully-connected sigmoidal layer
        self.layer3 = LogisticRegression(input=self.layer2.output, n_in=500, n_out=10)
        self.negative_log_likelihood = (
                self.layer3.negative_log_likelihood
            )
        #predicting the calss value and calculating errors
        self.predict=self.layer3.y_pred
        self.errors = self.layer3.errors
        # the cost we minimize during training is the NLL of the model
        #self.cost = self.layer3.negative_log_likelihood(y)
        # create a list of all model parameters to be fit by gradient descent
        self.params = self.layer3.params + self.layer2.params + self.layer1.params + self.layer0.params
        self.input = input


def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
                    dataset='/home/ubuntu/Desktop/ramya/cifar-10-batches-py/',
                    nkerns=[20, 50], batch_size=500):
    """ Demonstrates lenet on MNIST dataset

    :type learning_rate: float
    :param learning_rate: learning rate used (factor for the stochastic
                          gradient)

    :type n_epochs: int
    :param n_epochs: maximal number of epochs to run the optimizer

    :type dataset: string
    :param dataset: path to the dataset used for training /testing (MNIST here)

    :type nkerns: list of ints
    :param nkerns: number of kernels on each layer
    """

    rng = numpy.random.RandomState(23455)
    '''
    #print dataset
    print(datasets_0)
    print type(datasets_1)
    print type(datasets_0)
    print type(dataset)
    print type(datasets_0)
    print type(datasets_1)
    print type(datasets_0)
    '''
    print("hello")
    print(type(dataset))
    datasets_0={}
    datasets_1={}
    datasets_2={}

    datasets_0,datasets_1,datasets_2 = load_data(dataset)
    print(type(datasets_1))
    train_set_x, train_set_y = datasets_0
    valid_set_x, valid_set_y = datasets_1
    test_set_x, test_set_y = datasets_2

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0]
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    n_train_batches //= batch_size
    n_valid_batches //= batch_size
    n_test_batches //= batch_size

    # allocate symbolic variables for the data
    index = T.lscalar()  # index to a [mini]batch

    # start-snippet-1
    x = T.matrix('x')   # the data is presented as rasterized images
    y = T.ivector('y')  # the labels are presented as 1D vector of
                        # [int] lab

    ######################
    # BUILD ACTUAL MODEL #
    ######################
    #creating an object for cnn 
    classifier = cnn(
        rng=rng,
        input=x,
        batch_size=500,
        nkerns=[20, 50]
    )
    print('... building the model')
    # calculating cost
    cost = (
        classifier.negative_log_likelihood(y)
        
    )
    #test model
    test_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: test_set_x[index * batch_size:(index + 1) * batch_size],
            y: test_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )
    #validate model
    validate_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: valid_set_x[index * batch_size:(index + 1) * batch_size],
            y: valid_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )
    print(type(classifier.params))
    #finding gradient for parameters
    gparams = [T.grad(cost, param) for param in classifier.params]

    # train_model is a function that updates the model parameters by
    # SGD Since this model has many parameters, it would be tedious to
    # manually create an update rule for each model parameter. We thus
    # create the updates list by automatically looping over all
    # (params[i], grads[i]) pairs.
    updates = [
        (param_i, param_i - learning_rate * grad_i)
        for param_i, grad_i in zip(classifier.params, gparams)
    ]

    train_model = theano.function(
        [index],
        cost,
        updates=updates,
        givens={
            x: train_set_x[index * batch_size: (index + 1) * batch_size],
            y: train_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )
    # end-snippet-1

    ###############
    # TRAIN MODEL #
    ###############
    
    print('... training')
    # early-stopping parameters
    patience = 10000  # look as this many examples regardless
    patience_increase = 2  # wait this much longer when a new best is
                           # found
    improvement_threshold = 0.995  # a relative improvement of this much is
                                   # considered significant
    validation_frequency = min(n_train_batches, patience // 2)
                                  # go through this many
                                  # minibatche before checking the network
                                  # on the validation set; in this case we
                                  # check every epoch

    best_validation_loss = numpy.inf
    best_iter = 0
    test_score = 0.
    start_time = timeit.default_timer()

    epoch = 0
    done_looping = False
    #print(get_pickling_errors(classifier))
    while (epoch < n_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in range(n_train_batches):

            iter = (epoch - 1) * n_train_batches + minibatch_index

            if iter % 100 == 0:
                print('training @ iter = ', iter)
            cost_ij = train_model(minibatch_index)

            if (iter + 1) % validation_frequency == 0:

                # compute zero-one loss on validation set
                validation_losses = [validate_model(i) for i
                                     in range(n_valid_batches)]
                this_validation_loss = numpy.mean(validation_losses)
                print('epoch %i, minibatch %i/%i, validation error %f %%' %
                      (epoch, minibatch_index + 1, n_train_batches,
                       this_validation_loss * 100.))

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:

                    #improve patience if loss improvement is good enough
                    if this_validation_loss < best_validation_loss *  \
                       improvement_threshold:
                        patience = max(patience, iter * patience_increase)

                    # save best validation score and iteration number and save best model
                    #print(get_pickling_errors(classifier))
                    with open('/home/ubuntu/Desktop/ramya/best_model_cnn.pkl', 'wb') as f:
                        #print(get_pickling_errors(classifier))
                        pickle.dump(classifier,f)


                    best_validation_loss = this_validation_loss
                    best_iter = iter

                    # test it on the test set
                    test_losses = [
                        test_model(i)
                        for i in range(n_test_batches)
                    ]
                    test_score = numpy.mean(test_losses)
                    print(('     epoch %i, minibatch %i/%i, test error of '
                           'best model %f %%') %
                          (epoch, minibatch_index + 1, n_train_batches,
                           test_score * 100.))

            if patience <= iter:
                done_looping = True
                break

    end_time = timeit.default_timer()
    print('Optimization complete.')
    print('Best validation score of %f %% obtained at iteration %i, '
          'with test performance %f %%' %
          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    print(('The code for file ' +
           os.path.split(__file__)[1] +
           ' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)

def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('/home/ubuntu/Desktop/ramya/best_model_cnn.pkl'))

    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.predict)

    # We can test it on some examples from test test
    dataset='/home/ubuntu/Desktop/ramya/cifar-10-batches-py/'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print("Predicted values for the first 10 examples in test set:")
    print(predicted_values)


def experiment(state, channel):
    evaluate_lenet5(state.learning_rate, dataset=state.dataset)

if __name__ == '__main__':
    evaluate_lenet5()